A procurement lead at a mid-market manufacturer asks ChatGPT for the top three suppliers of industrial flow valves rated for sub-zero environments. Twenty seconds later, three companies are named in the answer. Two of them get a meeting that week. One of them does not exist in the answer at all, even though their product is technically the best fit.
This scenario is no longer hypothetical. It is happening every day across B2B procurement, and most enterprise commerce teams have not yet built the data infrastructure required to show up in it.
The discovery layer of commerce is changing. Google is still the dominant front door, but it is no longer the only one. Generative engines like ChatGPT, Perplexity, Claude, and increasingly Google’s own AI Overviews are becoming the place where research-stage and consideration-stage buying decisions begin. For B2B specifically, where buyers are technical, the products are complex, and a single recommendation can drive a six-figure deal, this shift is not a marketing curiosity. It is a structural change in how revenue gets sourced.
And the rules for showing up have changed.
AEO and GEO, briefly
Two terms are competing for the same idea. Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) both describe the practice of making content retrievable and citable by AI systems. The terminology is still settling. Gartner is currently using GEO. Some practitioners prefer AEO. The work underneath both is functionally the same.
The work is also meaningfully different from traditional SEO. Traditional SEO rewards keyword density, backlink authority, and page-level signals to a crawler. AI search rewards something else entirely: structured information that can be parsed, attributed, and synthesized into an answer. We have written about the practical shift this implies for content. The implications for product data are even more consequential, and almost no B2B teams have started addressing them seriously.
How AI engines actually retrieve product data
A generative engine does not browse your product detail page the way a human does. It does not scroll, hover, or read marketing copy. It performs retrieval against a structured representation of your content, synthesizes the relevant fragments, and constructs an answer. The fragments it retrieves are typically attribute pairs, named entities, schema-marked data, and structured FAQs. The fragments it ignores are unstructured marketing prose, image-only specifications, and dynamically rendered content that requires JavaScript execution.
Concretely, when a buyer asks an AI engine about industrial flow valves rated for sub-zero environments, here is what the engine is looking for inside the candidate products:
A clear product name and category, mapped to a recognized taxonomy.
Structured specifications including operating temperature range, materials, certifications, and standards compliance.
Schema markup that identifies what kind of object the page describes.
Citable, factual content that can be lifted as a quoted attribute.
Authority signals at the domain level. Recency, expertise markers, and third-party validation.
If a product detail page has the specifications buried in a downloadable PDF spec sheet, with a marketing description that says "engineered for the toughest environments," the AI engine cannot cite it. The product is effectively invisible in the answer. A competitor whose page lists "Operating temperature: minus 60 to plus 200 Fahrenheit" as a structured attribute gets cited instead.
This is the new ranking.
Where B2B product data falls short
Across the enterprise B2B catalogs we have audited in the past 18 months, a few patterns recur with uncomfortable consistency.
First, specifications live in PDFs rather than in structured product data. The spec sheet is a downloadable artifact, often a relic from print catalog days, and the actual website page contains only a teaser description plus a download link. AI engines do not parse PDFs reliably. They parse structured HTML. The PDF approach made sense in 2012. It is a citation killer in 2026. This is also the same problem that breaks recommendation engines and AI search inside the storefront. The fix solves both problems at once.
Second, attribute taxonomies are inconsistent across the catalog. Different product families use different attribute names for the same underlying property. Diameter, OD, outer diameter, size, and dimension all describe the same thing in different SKUs. To a human merchandiser, this is forgivable. To a retrieval engine, it means the catalog cannot be reasoned across.
Third, content is written for branding rather than for retrieval. The hero copy on the product page reads like an ad. The supporting copy explains why the company is great. The actual product attributes are three clicks away. An AI engine looking for substance finds adjectives.
Fourth, schema markup is missing or incomplete. Product schema, FAQ schema, organization schema, and breadcrumb schema all give retrieval engines explicit signals about what is on the page. Most B2B sites either skip schema entirely or implement it superficially with default values from the CMS.
What good looks like
A B2B catalog optimized for AI search looks different from one optimized for Google in 2018. The visible page may look similar. The underlying data is significantly more structured.
Every product page surfaces its specifications as structured attributes, not as a PDF link. Attribute names use a single canonical taxonomy across the catalog. Schema markup is implemented for Product, FAQPage, Organization, and where applicable HowTo. Content includes structured Q&A blocks that mirror how buyers actually ask questions of AI engines: "What is the operating temperature range," "What certifications does this product carry," "What is the lead time for orders of more than 500 units."
Authority signals at the domain level are reinforced through citations, structured data about the company, and content that establishes expertise. AI engines preferentially cite domains they have already determined are credible on a given topic.
And the content updates frequently enough that the engine treats it as current. Stale catalog content gets de-prioritized in retrieval the same way stale content gets de-prioritized in traditional search. The cadence of update is itself a signal, which means the integration layer underneath the catalog is part of the AI search story too. If product data updates flow from PIM and ERP in near real time, AI engines see a live catalog. If they flow in nightly batches, the engine sees yesterday’s answer. We have written about the integration architecture that makes this possible in the broader context of enterprise B2B commerce.
What to do this quarter
For enterprise teams new to AEO and GEO, the highest-leverage actions cluster into three categories.
Structure first. Audit how product specifications are surfaced on the site. If the data lives in PDFs, downloadable spec sheets, or image-only formats, prioritize moving it into structured HTML. The benefit compounds across AI search, internal site search, and recommendation engines.
Schema next. Implement Product, FAQPage, and Organization schema across the catalog. Validate the markup using the Schema.org validator and Google’s Rich Results tool. Confirm that AI engines can parse the structured data by spot-checking a sample of product queries in ChatGPT and Perplexity.
Content third. Add structured Q&A blocks to the highest-traffic product categories. Write the questions the way buyers actually phrase them in conversation, not the way internal teams describe the product. "How do I choose between Model A and Model B" is a better Q&A entry than "Comparison guide."
For teams ready to assess where they stand against this benchmark, the eCommerce technology assessment our team runs covers AEO and GEO readiness alongside the broader data and integration questions. The benefit of an outside assessment is honest scoring against criteria most internal teams have not yet had a reason to apply.
The buyers using AI search to research suppliers right now are not a small fringe. They are procurement leads, engineering managers, and category buyers at the kind of mid-market and enterprise accounts every B2B commerce team is trying to win. Whether your products show up in their answers is being decided right now, by infrastructure decisions that were made years ago. The good news is the gap is closeable. The work is not glamorous, but it is tractable, and the teams that close it first will dominate AI-mediated B2B discovery for the next decade.
FAQs
Q: What is AEO and how is it different from SEO?
A: AEO stands for Answer Engine Optimization. It is the practice of structuring content so that AI engines like ChatGPT, Perplexity, and Google AI Overviews can retrieve, attribute, and cite it in their answers. Traditional SEO optimizes for ranking on a search results page. AEO optimizes for being included inside a generated answer. The work is related but the techniques differ. AEO weights structured data, schema markup, and citation-worthy content far more heavily than backlink authority or keyword density.
Q: How do I get my B2B products to show up in ChatGPT and Perplexity?
A: Three actions move the needle most reliably. First, move product specifications out of PDFs and into structured HTML on the page. AI engines cannot retrieve reliably from PDF assets. Second, implement Product, FAQPage, and Organization schema markup across the catalog. Schema gives retrieval engines explicit signals about what is on the page. Third, add structured Q&A blocks written in the conversational phrasing buyers actually use when querying AI engines. Beyond these three, domain-level authority signals and update frequency contribute to retrieval priority.
Q: Is AEO replacing SEO for B2B?
A: Not replacing. Supplementing. Google still drives the majority of B2B search traffic, and traditional SEO remains essential. But the share of buyer research happening inside generative engines is growing fast, and that traffic does not show up in Google Analytics as referral data. Teams that ignore AEO are increasingly invisible to a meaningful and growing slice of their addressable market. The right framing is "and," not "or." Both deserve investment, and the work for AEO often improves traditional SEO as a side effect.